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holiglm (version 1.0.1)

Holistic Generalized Linear Models

Description

Holistic generalized linear models (HGLMs) extend generalized linear models (GLMs) by enabling the possibility to add further constraints to the model. The 'holiglm' package simplifies estimating HGLMs using convex optimization. Additional information about the package can be found in the reference manual, the 'README' and the accompanying paper .

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Version

Install

install.packages('holiglm')

Monthly Downloads

181

Version

1.0.1

License

GPL-3

Maintainer

Benjamin Schwendinger

Last Published

December 20th, 2024

Functions in holiglm (1.0.1)

update_objective

Update the Model Object
sign_coherence

Sign Coherence Constraint
upper

Upper Bound
k_max

Constraint on the Number of Covariates
reexports

Objects exported from other packages
rhglm

Random HGLM Data
solution.hglm

Extract Solution
rho_max

Constraint on the Pairwise Correlation of Covariates
scale_constraint_matrix

Scale Linear Constraint Matrix
coef.hglm

Extract Model Coefficients
group_equal

Group Equal Constraint
agg_binomial

Aggregate Binomial Data
active_coefficients

Obtain all Active Coefficients
bike

Bike Sharing Dataset
cov_matrix

Construct Covariance matrix
group_sparsity

Group Sparsity Constraint
group_inout

In-Out Constraint
as.OP.hglm_model

Convert to OP
hglm

Fitting Holistic Generalized Linear Models
linear

Linear Constraint
include

Include Constraint
hglmc

Generic Functions for hglmc Objects
hglm_fit

Fitting Holistic Generalized Linear Models
holiglm-package

Holistic Generalized Linear Models Package
predict.hglm

Predict Method for HGLM Fits
lower

Lower Bound
pairwise_sign_coherence

Pairwise Sign Coherence
hglm_model

Create a HGLM Model